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African Journal of Biotechnology Vol. 6 (10), pp. 1239-1247, 16 May 2007
Available online at http://www.academicjournals.org/AJB
ISSN 1684–5315 © 2007 Academic Journals
Full Length Research Paper
Genetic diversity of Annona senegalensis Pers.
populations as revealed by simple sequence repeats
(SSRs)
Kingdom Kwapata1, Weston F. Mwase1*, J. M. Bokosi2, M. B. Kwapata1 and P. Munyenyembe3
1University of Malawi, Bunda College of Agriculture, Forestry and Horticulture Department, P.O. Box 219, Lilongwe,
Malawi.
2 University of Malawi, Bunda College of Agriculture, Crop Science Department, P.O. Box 219, Lilongwe, Malawi
3University of Malawi, Bunda College of Agriculture, Department of Natural Resources Management, P.O. Box 219,
Lilongwe, Malawi
Accepted 19 April, 2007
Annona senegalensis Pers. is one of the wild fruit tree for domestication in southern Africa. An
assessment of the genetic diversity in A. senegalensis would assist in planning for future germplasm
collection, conservation and fruit domestication programmes. During 2004 to 2006 nine populations
were collected from different locations in Malawi and genetic diversity was evaluated using
microsatellites or simple sequence repeats (SSRs) developed in Annona cherimola. In total 23 alleles
were detected in the populations studied and genetic diversity parameters revealed high levels of
heterozygosity with 4.0 to 14 alleles per locus and the populations were genetically different by 19% as
given by the value of theta. Results demonstrated association between genetic and geographical
distance in the species indicating that large-scale geographical and ecotypic differentiation was
reflected by the SSR markers. The high genetic diversity is attributed to biological characteristics of the
tree species and habitat heterogeneity. The study has revealed evidence of application of SSR markers
from A. cherimola towards genetic fingerprinting of A. senegalensis. Implications of the SSR marker
data for optimizing genetic management of the species are discussed.
Key words: Annona, conservation, genetic diversity, heterozygosity, microsatellites, SSRs, population
INTRODUCTION
Annona senegalensis Pers. is one of the most important
wild indigenous fruit trees in southern Africa. It is a diploid
member of Annonaceae family which is one of the largest
tropical and subtropical plant families with about 2300
species of trees, shrubs and lianas. A. senegalensis is
native and common in savannas throughout tropical
Africa, from the Cape Verde Islands, the Nile and Upper
Guinea to Transvaal and Zululand (Ahmed, 1986). The
fruits of A. senegalensis form an important part of diet for
most indigenous populations in southern Africa. The fruits
are liked because of good flavour, sweetness and high
nutrition. The food value varies considerably, but most
*Corresponding author’s E-mail: westmwase@yahoo.co.uk. Tel:
(265) 1 277 361 Fax: (265) 1 277 364
forms have an abundance of carbohydrates, proteins,
calcium, phosphorus, iron, thiamine, niacin and riboflavin;
while some are rich in magnesium, ascorbic acid and
carotenes (Yoa and Wickramaratne, 1995). A.
senegalensis has several medicinal purposes including
use of parched green fruits to relieve diarrhoea and
dysentery. The bark is chewed to relieve stomachache,
while young boiled leaves, leafy twigs and roots are taken
to alleviate pulmonary complaints. Dried, powdered
leaves are regarded as purgative and as a remedy for
mucous diarrhoea. Venereal diseases and intestinal dis-
orders are treated with preparations of the roots
(Sofowora, 1993). Unopened flower buds are used both
in soup and seasoning of native dishes (Williamson,
1975). If domesticated A. senegalensis could therefore
considerably improve nutrition, health and incomes of
many rural people and thereby contributing to the improv-
1240 Afr. J. Biotechnol.
ement of people’s livelihoods in southern Africa.
In Malawi high anthropogenic pressure on land has
resulted in severe deforestation and rapid erosion of
genetic material of Annona species, unless conservation
efforts are made, the species is likely to disappear from
most forests (Campbell and Popenoe, 1988). In this
regard knowledge of genetic diversity of different popula-
tions is important to form a basis for conservation,
genetic tree improvement and promotion of domestication
of populations with desirable traits. Since the extent of
genetic diversity is not known, it is imperative to have an
elaborate strategy aimed at evaluating genetic diversity of
Malawian populations of A. senegalensis. Studies on use
of morphological traits and isozyme markers have been
reported on members of Annonaceae especially Annona
cherimola Mill. (Perfectti and Pascual, 2004; Cautin and
Agusti, 2005). However, most morphological traits are
highly influenced by environmental conditions or vary with
development stage of plant and isozymes are limiting due
to low levels of polymorphisms. Consequently DNA ba-
sed techniques such as microsatellites or simple seque-
nce repeats (SSRs) are effective in assessing genetic
diversity of plant species because they provide unlimited
potential markers to reveal differences at molecular level.
Simple sequence repeats or microsatellites consist of
variable numbers of tandemly repeated units each of 1 to
6 bp and are abundant throughout eukaryotic genomes
(Kijas et al., 1995; Kahl, 2001). Microsatellites are ampli-
fied using a polymerase chain reaction and they are later
detected using various methods such as fluorescent dye
labeling and silver staining. At present microsatellites are
the most preferred marker types because they are highly
polymorphic even between closely related lines, require
low amounts of DNA and can be easily automated, can
be exchanged between laboratories and are highly trans-
ferable between populations (Gupta et al., 1999). Another
advantage is that the PCR products of different markers
can be run on the same gel, saving time, labour and
money. Compared to other classes of markers SSRs
often carry high numbers of alleles at very low frequent-
cies or private alleles present in only one or few popula-
tions. This greatly contributes to the assessment of gene-
tic relationships among and within populations. One
disadvantage of microsatellites is the presence of null
alleles and these alleles are not amplified, hence they
can not be scored. These can therefore lead to an under-
estimation of heterozygosity. The major disadvantage in
the use of microsatellites markers is the considerable
initial investment needed to develop and map them.
This study is the first attempt to study the genetic
diversity of A. senegalensis from Malawi using simple
sequence repeats. The objective of the study was to map
out bio-geographical distribution of A. senegalensis in
Malawi and determine genetic diversity among the popu-
lations using simple sequence repeats. The results could
be useful in guiding future conservation and tree improve-
ment programme of this species.
MATERIALS AND METHODS
Study area
The study was conducted in Malawi during 2004 to 2006. Field
plots of 32 m radius were established at nine different locations
throughout the country (Figure 1). The identification of the sites was
done at random, but taking into consideration geographical set-up
of country, whereby all three regions were each represented by
three sites. The names of populations are Mapapa-Karonga,
Mphopha-Rumphi, Kaning’ina-NkhataBay in the Northern region;
Bunda-Lilongwe, Chimaliro-Kasungu and Chibothera-Nkhotakota in
Central Malawi and Mkulumadzi-Neno, Likhubula-Mulanje and
Kuchawe-Zomba in Southern Malawi. Selected populations were
heterogeneous for forest habitat type and altitude (Table 1).
Collection of leaf samples
A total of 135 leaf samples were collected from 9 localities with
15 individuals representing a population (Table 1) along the
geographical range of natural distribution of A. senegalensis in
Malawi. Young fresh leaves of 2 - 3 weeks old of approximately 4 x
2 cm in length and width respectively were collected from the nine
locations. There were three plots per population and five individuals
represented a plot thus giving a total of 135 samples for all
populations. Leaves from individual trees were plucked off from the
base of their peduncle and inserted into 8 cm plastic tubes and then
placed in a cooler box containing super freeze coolant before being
sent to Stellenbosch University in the Republic of South Africa for
characterization.
DNA extraction
Total genomic DNA extraction was extracted from frozen leaf
samples. About 200 mg leaf sample was used in DNA extraction
using Nucleospin 8 Plant Extraction Protocol (Macherey–Nagel,
2004) following the manufacturer's instructions with minor modifi-
cations. The DNA sample was incubated at room temperature for 1
min and centrifuged at 13,000 rpm to recover pure DNA. Following
precipitation of impurities and RNase digestion isolated DNA was
resuspended in 200 l Tris-EDTA buffer and stored at -20oC until
further analysis. DNA concentration was determined by both Nano-
drop spectrophotometry at 260/280 nm and ethidium bromide stain-
ing on a 1% agarose gel electrophoresis (Sambrook et al., 1989)
Amplification and genotyping of microsatellites
The PCR reaction mixture contained 10 mM Tris-HCl, pH 9.5, 50
mM KCl, 200 µM dNTPs, 2 mM MgCl2, 0.2 µM of each forward and
reverse primer, 1.5 units Taq polymerase (Promega), and 2 ng DNA
templates in 20 µl total volume. The amplifications were conducted
with a Perkin- Elmer 9700 Thermal Cycler (Applied Biosytems, CA,
USA). The programme consisted of an initial 5 min at 94oC that was
followed by 35 cycles of 30 s at 94oC, 30 s at 55oC and 1 min at
72oC with a final extension period of 10 min at 72oC and lastly
storage at 4oC until when the PCR products were required for use.
A negative control with only the reaction mixture excluding DNA
was also included in each experiment. Four Annona-specific
primers with motif repeat rich in (CT)20, (GA)14, (CT)14 and (CT)10
were developed from Annona cherimola (Escribano et al., 2004)
and used to detect the genetic diversity of A. senegalensis (Table
2). The SSRs were screened on Genetic Analyzer model 3730 XL
automated DNA sequencer, G5 dye set running an altered geno-
typing module that increased the injection time to 30 s and injection
voltage to 3 kV. About 1 µl of PCR product and 2µl of each of the
Kwapata et al. 1241
Figure 1. Map of Malawi showing the collection sites of
Annona senegalensis.
Table 1. Nine Annona senegalensis populations collected in Malawi with climatic data for localities.
Population Region Temperature (oC) Mean annual rainfall (mm) Altitude (m) Soil pH
Mapapa, Karonga Northern 20 1025 561 6.3
Mphopa, Rumphi Northern 20 890 1434 5.7
Kaning’ina, Nkhatabay Northern 24 1009 700 5.3
Chimaliro, Kasungu Central 22 762 1354 6.4
Bunda, Lilongwe Central 22 925 1100 5.8
Chibothera,Nkhotakota Central 25 1440 503 4.7
Kuchawe, Zomba Southern 24 1000 1112 5.4
Likhubula, Mulanje Southern 20 2300 1400 5.6
Mkulumadzi, Neno Southern 25 950 990 5.4
Table 2. Locus name, primer sequence, repeat motif and type (I, imperfect; P, perfect) of the
microsatellites analyzed.
Oligo-name Locus Sequence (5’-3’) Repeat No of bases Type G5 Dye
Annona1 F
Annona 1 R
M01 CTCTTCAAAGGTACGACTTC
TTGAGAAAAGGATAAGGATT
(CT)20
20
20
I black
Annona 4 F
Annona4 R
M04 ATTAGAACAAGGACGAGAAT
CCTGTGTCTTTCATGGAC
(GA)14
20
18
P green
Annona 6 F
Annona6 R
M06 GGCATCCTATATTCAGGTTT
TTAAACATTTTGGACAGACC
(CT)14
20
20
P blue
Annona11F
Annona11R
M11 TACCTCTCGCTTCTCTTCCT
GATGATTAGACACAAGTGGATG
(CT)10
20
22
I red
1242 Afr. J. Biotechnol.
Figure 2. Amplification of four primers at three loci in Annona
senegalensis and A. cherimoya.
four primers were loaded on 10 µl of HiDi Formamide and 1.5 µl of
Gene Scan–500 LIZ size standards (Applied Biosystems) marker
with an orange dye.
Data analysis
Automatic genotyping and scoring of allele size were performed by
the GeneMapper® version 3.5 software. The FSTAT software version
2.9.3 (Goudet, 2001) was used to test genetic diversity parameters
such as Hardy-Weinberg Equilibrium test, heterozygote deficit,
population differentiation, allelic and genotype frequencies. Statis-
tical analyses were done using GENEPOP 3.1 (Raymond and
Rousset, 1995) and FSTAT. The extent of genetic differentiation be-
tween populations was estimated from the theta estimator of FST
(Weir and Cockerham, 1984). Nei's genetic distance (Nei, 1972)
matrices were computed to determine the genetic distance between
genotypes. This method is appropriate for populations shaped by
diverse evolutionary forces. The formula used to compute Nei's
distance (D) is:
ln
D I
=
Where pxi = frequency of allele x in population i, and pxj = the frequ-
ency of allele x in population j. The genetic distances were then
clustered using unweighted pair-group method with arithmetic
averages (UPGMA).
RESULTS AND DISCUSSION
Microsatellite amplification
Figure 2 shows typical electrophoregrams obtained with
multiple sample loading of the four primers that were test-
ed on (A) Bunda population, (B) A. cherimoya from which
these primers were adopted, (C) Karonga population and
(D) Nkhotakota population. Three loci were examined
and the pooled analysis of the primers showed that the
green primer was the best followed by the blue and then
red. The black primer did not amplify in A. senegalensis
but only in A. cherimoya from which the primers were
developed from.
Gene and genotype frequencies
The total number of alleles detected across all the loci
was 23 (Table 3). Gene and genotype frequencies evalu-
ated at the three loci revealed that locus M06 had four-
teen alleles and was the most polymorphic while M11
was the least polymorphic locus.
All the three primer pairs amplified multiple fragments
in the nine populations used indicating high polymor-
phism with putative alleles ranging from 4 to 14 per locus.
The highest allelic diversity of 14 alleles was observed for
the perfect binucleotide SSR locus MO6 and the lowest
allelic diversity for the imperfect binucleotide SSR locus
MO11. Except for locus M04 the other two loci had higher
numbers of observed alleles than reported in the original
publication (Escribano et al., 2004). This could be attribu-
ted to differences in phylogenetic distances between the
two species A. cherimola and A. senegalensis. The SSR
markers described herein had twice as many alleles as
isozyme markers suggesting that SSR loci would better
detect fine-scale genetic differentiation. The average
number of alleles detected was 5.7 per polymorphic locus
Table 3. Distribution and frequency of alleles for three micro-
satellite loci in A. senegalensis.
Locus Allele Frequency Number of alleles
M04
As109
As113
As115
As119
As121
0.01
0.02
0.45
0.49
0.03
5
M06
As211
As217
As218
As219
As221
As223
As225
As227
As231
As232
As233
As235
As237
As241
0.01
0.19
O.02
0.42
0.04
0.12
0.05
0.02
0.01
0.01
0.01
0.02
0.07
0.01
14
M11
As169
As171
As173
As175
0.39
0.02
0.58
0.01
4
Total 23
which is higher than 2.05 ± 0.25, the range reported in A.
cherimola through use of isozymes (Perfectti and
Pascual, 2004) and other woody long-lived perennial
plants (2.19 ± 0.09; Hamrick and Godt, 1990). The higher
allelic richness in SSRs constitutes an advantage of SSR
over other markers such as isozymes, random amplified
polymorphic DNA (RAPDs) and amplified fragment length
polymorphism (AFLP). Several studies have shown that
SSR markers detect higher levels of polymorphism,
providing a higher level of information per single marker
(Powell et al., 1996; Pejic et al., 1998). Our data on A.
senegalensis is in agreement with these findings.
The most frequent allele was As173 which had a
frequency of 58% (Table 3). This was the only allele that
was present in all the nine provenances. The presence of
allele As173 in all nine populations is interesting. A
relationship between region of origin and genotypic or
allelic frequencies has been observed in a wide range of
plants, both cultivated and wild, for example, in sorghum
Kwapata et al. 1243
(Morden et al., 1990), trees such as chestnut (Huang et
al., 1994). The causes of these relationships are subject
of debate, Nevo and Beiles (1989) found that environ-
mental and ecological factors are more important than
geographic distances while Li and Rutger (2000) found
that epistasis and selection of multiple gene complexes
was responsible for macro-geographic differentiation.
The other alleles that were found in high frequencies
were alleles As119, As115 and As219 which had frequ-
encies of 49, 45 and 42% respectively.
The percentage of unique and localized alleles across
the populations was 15%. This means that 15% of the
alleles detected across loci are private. In the study priv-
ate alleles are in low frequency which may reflect new
mutations in isolated populations. A number of popula-
tions for instance Nkhotakota, Mulanje and Rumphi have
loci fixed for single alleles and in some cases this may be
apparent rather than real. Populations displaying unique
alleles may represent wild relatives suggesting that the
ancestral genotypes containing these alleles are not
represented in the other collection of populations. Althou-
gh populations displaying unique alleles may represent
wild germplasm, it is also possible that the population
specific alleles were derived from a mutation event since
SSRs loci are known to have a high rate of mutation per
locus per generation of 25 X10-5 to 1 X10-2 (Weber and
Wong, 1993). The distribution of these private alleles also
suggests a rare mutation, low frequency allele from A.
senegalensis or mating with a close relative. Both of the
two private alleles As241 and As232 are in very low
frequency of only 1%. In this research work, smaller
samples were used, private allelic richness is affected by
size of the samples whereby large samples are expected
to have more private alleles than small ones, on the other
hand, intensive sampling of genetically similar popula-
tions may reduce the number of alleles private to any
population (Kalinowski, 2004). The two private alleles
were geographically restricted hence the microsatellite
loci presented could be used to investigate the evolu-
tionary history or phylogeography of A. senegalensis.
There were a total of 34 genotypes detected across all
three loci. Loci M06 was the most genotypically rich loci
with 22 genotypes, followed by loci M04 and finally loci
M11 with 7 and 5 genotypes respectively. The most freq-
uent genotype was As 169/173 which had the frequency
of 53%. This genotype is found in all populations except
for Neno. This genotype is found in the heterozygous
state and it could be responsible for a very important trait
that is probably why it is more abundant in most popu-
lations.
Microsatellite amplification across species would allow
the detection of infra-specific taxa (Alvarez et al., 2001),
the separation of morphologically similar taxa or an
elucidation of the nature of morphologically distinct, yet
genetically close, taxa. It has been shown that micro-
satellite primers developed for a distinct species such as
A. cherimoya can be useful for genetic analysis in related
1244 Afr. J. Biotechnol.
Table 4. Genetic diversity indices and Hardy-Weinberg equilibrium P-values in nine A.
senegalensis populations.
Population Average gene diversity (H) Average difference HWE p-value
Karonga, Mapapa 0.48 50.88 0.990
Rumphi, Mphopa 0.58 60.32 0.860
Mulanje, Likhubula 0.53 56.18 0.770
Neno, Mkulumadzi 0.39 41.34 0.440
Nkhata-Bay, Kaning’ina
0.62 66.36 0.440
Kasungu, Chimaliro 0.61 64.95 0.200
Lilongwe, Bunda 0.52 54.96 0.180
Nkhotakota, Chibothera 0.57 60.9 0.110
Zomba, KuChawe 0.22 23.49 0.007*
Means 0.50 ± 0.22 53.26 ± 11.72
Means are followed by ± standard errors of the mean.
*The population was not in Hardy-Weinberg Equilibrium at p < 0.05.
species (Davis and Strobeck, 1998), but successful trans-
ferability depends upon the evolutionary distance bet-
ween source and target species (Peakall et al., 1998;
Roa et al., 2000; Rosetto, 2001). However, successful
cross-amplification simply indicates that the flanking
regions are conserved, but it does not tell anything about
the character and structure of the fragment.
Hardy-Weinberg equilibrium test and genetic
diversity
The results for test of Hardy-Weinberg equilibrium (HWE)
were not significant for all the populations except for
Kuchawe Zomba population which had statistically
significant heterozygote deficiency from the HWE. The
genetic diversity analysis of A. senegalensis at the three
loci (M04, M06 and M11), revealed that Nkhatabay and
Kasungu were the populations with the highest genetic
diversity of H=0.62 and H= 0.61 respectively (Table 4).
A specific U test (Rousset and Raymond, 1995) for
heterozygote deficiency indicated statistically significant
deficit for the Zomba population. No loci exhibited
deviation resulting from heterozygote excess. The most
common cause for heterozygote deficiency in SSR
marker studies is the presence of null alleles, Null alleles
occur when there is a mutation within the DNA sequence
complementary to one or both primers, preventing PCR
amplification of the SSR sequence (Callen et al., 1993).
Other causes of heterozygote deficiency include selection
or hitchhiking on a linked locus under selection, deviation
from panmixis, or population admixture. Of the three,
selection seemed to be a likely cause of HWE deviation
as the Zomba population is under farmers’ fields as
opposed to other populations occurring in natural forests.
The genetic diversity analysis of A. senegalensis at the
three loci (M04, M06 and M11), revealed that Nkhatabay
and Kasungu were the populations with the highest
genetic diversity of H= 0.62 and H= 0.61 respectively
(Table 4). Populations of A. senegalensis from Nkhata-
bay and Kasungu were from protected forests which are
contiguous as compared to woodlands on farm land.
Distribution range and population size have been
identified as major correlates of within population genetic
variation in tropical tree species with restricted popula-
tions showing significantly less variation than those with a
broader distribution. Forest fragmentation affects allelic
diversity through structure of populations and gene flow.
Aldrich and Hamrick (1998) found that gene flow from
outside the sampling area was three-times greater in
contiguous than remnant forests as such large contig-
uous forest populations will usually have a greater diver-
sity of alleles compared to small fragmented populations.
The high genetic variation in A. senegalensis is attributed
to its wide geographic distribution and population size.
Karron et al. (1988) suggested that not only do the geo-
graphic distribution and population size influence level of
genetic variation, but habitat heterogeneity and historical
distribution. The high levels of allelic diversity, along with
the high levels of heterozygosity can mainly be attributed
to biological characteristics such as predominantly out-
crossing, anemophily, and being a perennial woody plant,
all of which contribute to accumulation and retention of
genetic variation (Hamrick and Godt, 1989).
Zomba had the lowest genetic diversity (H= 0.22) and
this low level of genetic diversity suggests that there is a
lot of inbreeding due to its small population size. This is
also causing their population to experience genetic drift.
Stochastic forces such as small dispersed populations
may face genetic drifts and bottlenecks which are import-
ant in driving their population structure and evolution of
Annona lineages. Apart from genetic drift there is also
evidence to suggest that this population is probably the
only one apart from Neno that is under going consi-
derable degree of selection. This is evident by the small
size of the population with low genetic variation and it is
Kwapata et al. 1245
Figure 3. Dendrogram of nine A. senegalensis obtained with three A. cherimola SSR markers.
on a managed farmland. There is evidence to suggest
that the small population size of Zomba is not as a result
of genetic failure on the part of the population but rather
as a result of a population bottleneck that must have
occurred in site.
The analysis for the overall diversity of A. senegalensis
across loci and population as given by Nei’s estimate of
heterozygosity is 62%. The analysis further revealed that
diversity within populations (50%) is greater than across
population (11%). The gene flow across population of all
three loci as estimated from FST was 0.025. For such
highly polymorphic loci, any apparent population structure
based on finite samples must be interpreted cautiously
due to stochastic sampling errors in allele frequency
estimates and the strong likelihood that some rare alleles
may be missed. This low genetic differentiation among
population suggests that there was more gene flow for
sites within agroecological region because of their close
proximity. This high level of gene flow indicates that most
populations are randomly mating, with very little selec-
tion. Avearge heterozygosity of 62% suggests that A.
senegalensis can respond favorably to selection provid-
ing room for species conservation, crop improvement
through breeding and other biotechnology techniques.
The low level of differentiation across A. senegalensis
populations warrants conservation of this species to
maintain its genetic richness. The high genetic diversity
within populations suggests that a large number of
individuals have to be sampled from fewer populations for
conservation purposes. The advantage of species with
great genetic diversity like A. senegalensis is that it can
easily adapt and conform to a wide range of environ-
mental conditions as opposed to non-genetically diverse
species such as those of the domesticated crops, which
can not survive in a wide range of environments without
human supervision. The practical usefulness of this infor-
mation is that it will help in making rational decision as to
which populations to prioritize in terms of conservation
and domestication. The populations with the highest
genetic diversity, namely Kaning’ina-Nkhatabay and
Chimaliro-Kasungu need to be prioritized in terms of in
situ conservation of A. senegalensis.
Geographical and genetic distance among
populations
The analysis of genetic distance has revealed some
genetic structure of A. senegalensis populations in
Malawi. A mantel matrix correspondence test conducted
to assess the relationship between geographical distance
and genetic distance (data not shown) demonstrated that
there was a significant correlation between the two dista-
nce matrices (r = 0.48, p < 0.05). The genetic population
of A. senegalensis has three main clusters (Figure 3).
Most of the populations from northern and central
Malawi formed one cluster while southern Malawi popula-
tions formed the second cluster. Each cluster represents
populations that are congenial. Cluster 1 consists of
Karonga, Rumphi, Lilongwe, Nkhotakota and Kasungu,
while cluster 2 consists of Mulanje, Zomba and Neno.
Nkhatabay population from the lake shore area forms its
own cluster. This pattern shows that distance has a role
to play in the clustering of the provenances. As it is
shown populations that are close to each other in
distance share the same or almost similar genotypes as
opposed to populations that are further apart. It is likely
that the uniquely different environment of the lakeshore
area could have contributed to diversity of the population
hence clustering away from geographically closer popula-
tions. All the populations from southern Malawi from the
same agroecological zone and geographically closer to
each other form one cluster. The reason for this obser-
vation is that populations that are closer in terms of physi-
1246 Afr. J. Biotechnol.
cal distance may be closely related because of high
chances of gene flow. Connectivity between populations
as a result of gene flow between populations in the same
cluster is likely to be important in maintaining genetic
diversity and minimizing genetic drift. Apart from dist-
ance, the determination of clustering provenances toge-
ther could also be attributed to environmental similarities.
For example Mulanje and Zomba have similar environ-
mental conditions in the sense that they are both
highland populations. The close relationship bet-ween
central and Northern Malawi populations might be
explained by either historical relationship in probably in
sharing recent common ancestry or more likely geogra-
phical proximity and large population sizes which favour
genetic interchanges. This information help in making a
rational decision regarding prioritizing populations which
require conservation. This implies that populations of A.
senegalensis in Malawi can not be considered a single
panmictic unit although the genetic differentiation is low.
For purpose of conservation of genetic resources seed
collection need to be done across different populations to
ensure a more representative sample of genetic variation.
Conclusion
The results have demonstrated that there are significant
differences in the genetic diversity of populations of A.
senegalensis and SSR have proved to be suitable for
characterizing A. senegalensis. The higher level of poly-
morphism revealed by SSR indicated that populations
could be identified and information on genetic diversity
obtained from relatively few loci. The high level of genetic
diversity is due to out breeding nature of A. senegalensis
and this is a typical characteristic of wild tree species.
The immense pool of genetic richness exhibited by A.
senegalensis provides an opportunity for potential tree
improvement, as well as a genetic conservation program-
mme. The study also shows some evidence of high
cross-species transferability of A. cherimola SSRs in A.
senegalensis for detection of genetic diversity. For
conservation purpose priority should be given to popula-
tions that are more genetically diverse and these include
Nkhatabay, Kasungu, Rumphi and Nkhotakota.
ACKNOWLEDGEMENTS
Kingdom Kwapata was supported by the Norwegian
Research Council for Higher Education (NUFU) project
PROMA 69/2003 coordinated by Dr. Maigull Appelgren
from Norway and Prof M. Kwapata in Malawi. We wish to
express our gratitude to Carel van Heedern of Stellen-
bosch University in South Africa and his team for assis-
tance in genotyping of the simple sequence repeats. We
thank Grace Lameck for help with data collection and
Wisdom Changadeya at BIO-EROC, Zomba in Malawi for
assistance in data analysis. Goodson Dawa and Lester
Kalaundi are acknowledged for their help in collecting
and processing leaf samples.
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